1. Field of the Invention
The present invention relates to a system and method for identifying an image, and more particularly, to a system and method for identifying an image based on singular value decomposition (SVD) in order to identify images transformed by duplication and malicious attack, or according to a type of Internet and a reproducing device.
2. Description of the Related Art
As the demand of digital contents increases, an enormous amount of multimedia contents including video, audio, and image have been produced, distributed and serviced constantly. Among the multimedia contents, numerous digital still images (hereinafter images) have been explosively produced and distributed due to the popularization of high-performance portable digital cameras and the price drop of large capacity storage devices or portable storage mediums. There were many researches in progress to develop a technology for effectively searching a target image from numerous images or accurately identifying the target image from others. Such a technology refers to an image searching technology.
Conventionally, many image searching methods using meta data (key word) or contents of an image have been introduced. Since such conventional image searching methods use previously inputted information or original information included in an image, for example, color and texture, the conventional image searching methods were only useful to search an original image which is not transformed or modified.
The conventional image searching methods, however, cannot be used if the image is unlawfully modified through duplication or malicious attack, or if the original characteristics of the image, for example, the size, the format, and the quality, change according to a type of Internet or a reproducing device. In order to search such modified image, a technology for identifying a modified image is needed. Such a technology refers an image identifying technology.
Various technologies for identifying a modified image have been introduced. The image identifying technology has been classified into a feature point based image identifying technology and a non-feature point based image identifying technology.
Jin. S introduced one of representative non-feature point based image identifying methods, which uses a random transform scheme. After modifying or compressing images using an affine transform scheme, a Gaussian filter, a median filter, a cropping scheme, and a JPEG scheme, a simulation of identifying the modified images using the Jin's method is performed.
In the Jin's method, original characteristics of an image are defined as a finger print, and the finger print is formed of 400-bit data per one image. The overall performance of Jin's method is good. However, the Jin's method is too weak to identify an image modified by the cropping scheme or to identify a geometrically modified image.
Lowe introduced scale invariant feature transform (SIFT) as a feature point based image identifying method. In the SIFT, the identity of images is determined using a feature point and values adjacent to the feature point.
The SIFT is good to identify an image modified by cropping or a partially covered image. However, the SIFT is too weak to identify a geometrically modified image, for example, a tilted image, or a perspective-transformed image. Furthermore, the SIFT requires a complicated calculation process and a long time to determine the similarity of two feature points.